Instructions to use ProbeX/Model-J__ResNet__model_idx_0799 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0799 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0799") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0799") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0799") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2f706a3804a93c958e77b867ff992ca80c45cfe8dbb304c51ce157fc8945054
- Size of remote file:
- 5.37 kB
- SHA256:
- 8c1efe80b4e9e20083db4e339d9635141defd25f999c476bd7b79fb41e8ceb3b
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